The present invention relates to a method and a device for identifying or predicting process parameters of an industrial time-variant process.
In regulating and controlling industrial processes, in particular primary-industry plants such as steel works, it is frequently necessary to predict certain process parameters or to identify, i.e., determine, process parameters that cannot be measured directly. In doing this, it is desirable to also identify process parameters that can be measured through technical means, but only with complicated and therefore expensive measurement techniques.
The identification of process parameters on the basis of a model is known. In this case, input quantities, or the input quantities relevant for the process parameter to be identified, are supplied to a process model that is usually simplified. However, this known method frequently causes problems in primary-industry plants. The fact that identification errors, or insufficient identification accuracy, result in high costs due to the production of rejects is characteristic of primary-industry plants, in particular steel works. The occurrence of these problems is facilitated in particular by the fact that some faults change quickly in primary-industry plants, in particular steel works, so that goods of insufficient quality may be produced during the time that is needed to adapt the process model to the new input quantities. This problem affects, in particular, rolling mills in which the operating state changes abruptly due to rolls, e.g., of a new rolling strip that is made of a new material or has a different thickness than the previous strip.
An object of the present invention is to provide a method and a device that make it possible to quickly adjust identified or predicted process parameters to varying operating states of the corresponding process.
According to the present invention, this object is achieved by providing a method and a device for identifying or predicting process parameters of an industrial process, in particular a primary-industry plant, having in particular quickly varying process parameters or disturbances affecting the process, with the process parameters to be identified being determined by a process model as a function of measured values from the process, and with the process model having at least one time-invariant or one largely time-invariant process model, representing an image of the process averaged over time, and at least one time-variant process model that is adjusted to at least one time constant of a disturbance or of a variation in parameters of the process. This method has proven to be especially advantageous for identifying or predicting process parameters of a time-variant process. In this case, disturbances are interpreted as variations in the process parameters and modeled with variable model parameters, just like actual variations in the process parameters.
In one advantageous embodiment of the present invention, each significant constant of the process is assigned, in relation to the variation in the process parameters to be identified, a time-variant model that is adjusted to the corresponding time constant. By modeling each significant time constant, the process model is able to track each essential variation in the process parameters. This procedure thus makes it possible to quickly track the process model when the process undergoes rapid changes, caused, for example, by disturbances.
In a further advantageous embodiment of the present invention, the time-variant model is adjusted to a time constant, a variation or disturbance in the process in relation to the variations in the process parameters to be identified or predicted, by on-line adaptation of the time-variant model, with the cycle time of the on-line adaptation being advantageously adjusted to the time constant. Designing the time-variant model in the form of a neural network has proven to be especially advantageous.
In rolling mills, it has proven to be especially advantageous when the fastest model, i.e., the model that undergoes the most training cycles, is adapted or trained according to each rolling belt, in particular according to each rolling belt with new characteristics. Using one time-invariant model and two time-variant models in rolling mills has also proven to be advantageous.